The COVID-19 pandemic has spurred research exploring the impact of government policies and governance structures on pandemic control. While some studies focus on the efficacy of specific policies, others examine the role of political regimes and governance quality. Existing literature presents mixed findings, with some suggesting that more expansive governments or even those scoring poorly on government effectiveness, trust, and freedom might respond more effectively. This study aims to comprehensively investigate the relationship between good governance and COVID-19 control across 185 countries—a larger sample than previous studies. Unlike some prior research focusing on fatalities, this study examines the spread of the pandemic using COVID-19 positive and growth rates. The study draws upon the established arguments from medical historians and social scientists emphasizing the crucial role of good governance in epidemic control, particularly highlighting the need for effective governmental intervention to reduce the reproduction number of infectious diseases. The COVID-19 pandemic's unprecedented scale serves as a critical testing ground for this hypothesis, given the need for public health measures that often restrict personal freedoms and necessitate public trust in governmental commitments. The authors posit that effective governance enhances public compliance with these measures, leading to better pandemic control.

The paper reviews existing literature on the COVID-19 pandemic, categorizing it into two main strands: studies analyzing the socioeconomic and behavioral impacts of specific government policies, and those examining the influence of political regimes and governance on pandemic control outcomes. The authors highlight studies comparing democratic and authoritarian regimes, those examining the relationship between regime type and policy efficacy, and those exploring seemingly paradoxical findings that countries with poorer governance scores might exhibit quicker and more decisive responses. The review also includes research examining variations in government policies across European democracies, and those finding negative associations between COVID-19 fatalities and government effectiveness. The existing literature serves as a backdrop for the current study, which aims to provide a more comprehensive, large-scale analysis of the relationship between good governance and the spread of COVID-19, using both positive and growth rates as measures of pandemic spread.

The study uses data from the World Bank's Worldwide Governance Indicators (WGI) database for 2018, employing government effectiveness as the primary indicator of good governance. Additional WGI indicators such as rule of law, regulatory quality, control of corruption, voice and accountability, and political stability are also considered. Data on COVID-19 positive and growth rates (April-September 2020) were obtained from the WHO, Our World in Data, and the Johns Hopkins University COVID-19 dashboard. The positive rate is calculated as (Total positive cases)/(Total tests conducted × 100), while the growth rate is (Ct−Ct−1)/Ct−1 × 100, where Ct represents total confirmed cases per million population. The study includes various geographic and demographic control variables (ruggedness, landlocked status, distance to coast/river, latitude, air pollution levels, urban population percentage, median age, Polity ranking, and log GDP per capita). The analysis employs a sample of 185 countries. The study uses ordinary least squares (OLS) regression models with robust standard errors. The models are progressively expanded, starting with a base model including only government effectiveness and continent fixed effects, then adding geographic controls, and finally incorporating demographic and socioeconomic controls. Similar models were estimated using the COVID-19 growth rate as the dependent variable. Robustness checks involve sub-sample analysis (excluding countries from different continents and the OECD) and using alternative WGI constituents as explanatory variables. Violin plots visually demonstrate the relationship between good governance measures and the COVID-19 positive and growth rates.

The study's key findings reveal a significant negative relationship between government effectiveness and both the COVID-19 positive and growth rates. This relationship persists across various models, even after controlling for continent fixed effects and numerous geographic, demographic, and socioeconomic factors. The negative relationship is observed consistently across multiple months (April-September 2020) for the COVID-19 positive rate, indicating that higher levels of government effectiveness are associated with lower infection rates. While the relationship for the COVID-19 growth rate is significant in fewer months, the overall negative association remains robust when using average values across the six months. Beta coefficients quantify the effect sizes, showing that a one standard deviation increase in government effectiveness is associated with a substantial decrease in both the positive and growth rates. Sub-sample analysis, excluding various continental and regional groups, strengthens these findings. Additional analyses using other WGI constituents as explanatory variables also demonstrate consistently negative relationships with the COVID-19 positive rate, although the significance varies across different measures. Visualizations (violin plots) clearly illustrate the distribution differences between countries with above-average and below-average governance scores, confirming the inverse relationship.

The findings strongly support the hypothesis that better governance is associated with better COVID-19 control. This relationship appears to operate through enhanced policy implementation and increased public trust, leading to greater compliance with public health measures. The study’s findings are consistent with previous research on the link between good governance and the control of other epidemics (e.g., HIV). The robustness of the findings across different models and sub-samples lends significant credibility to the results. The stronger and more persistent relationship observed for the COVID-19 positive rate compared to the growth rate may reflect the initial phase of the pandemic, where compliance was likely higher due to heightened fear and uncertainty. As the pandemic progressed, compliance might have waned, leading to a weaker relationship between governance and the growth rate. The study addresses, and to an extent refutes, the seemingly contradictory findings of previous research suggesting that countries with lower governance scores might respond more effectively. While this might be attributed to early responsiveness, the current study underscores the long-term importance of good governance for sustained pandemic control.

This study provides strong empirical evidence of a significant inverse relationship between the quality of governance and the spread of COVID-19 across a large sample of countries. The findings highlight the crucial role of effective governance in managing pandemics, emphasizing the need for robust institutions, capable leadership, and public trust. Further research could explore the causal mechanisms in more detail, examining specific policy interventions and their effectiveness across different governance contexts. Longitudinal studies incorporating panel data could enhance the understanding of the dynamic relationship between governance and pandemic spread. Future research should also explore the impact of different governance factors on compliance behavior.

The study utilizes cross-sectional data, limiting the ability to establish causality definitively. The use of lagged independent variables helps mitigate concerns about reverse causality, but a panel data approach would be beneficial. The reliance on self-reported governance indicators (WGI) might introduce measurement error. Although the study employs various controls, some unobserved factors may still influence the relationship between governance and pandemic spread. The study’s findings might not be generalizable to all types of pandemics, given the specific context of COVID-19.